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Proceedings Paper

Person re-identification algorithm based on the fusion of deep feature and LOMO feature
Author(s): Zi Liang; Jianning Chi; Ruzhao Hua; Yue Zhang; Wenhao Wang
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Paper Abstract

Person Re-identification is a sub-problem of image retrieval, using computer vision techniques to judge whether a certain identical pedestrian exists among different images or video sequences, which has attracted more and more attention of researchers. In this paper, regarding the fact that under non-overlapping multi-camera, traditional handcrafted features have a limited presentation power in re-identifying the pedestrians and that deep features have complicated parameters while training. A re-identification method based on the deep fusion of handcrafted features and deep features was proposed, which cut down the number of parameters but still guaranteed the accuracy, achieving the advancement of both precision and capacity. In our model, the LOMO algorithm is used to extract the handcrafted features from the images first. Then, the dimensionality of those features are reduced by Guassian Pooling for efficiency. After that, they are connected to the deep fusion network with the deep features extracted from the same images by a modification of ResNet50. Finally, the fused features are sent to the classifier for the re-identification. In the training process, we proposed a training strategy called Gradient Freezing after studying the training details in the application of transfer learning on neural network. Experiments have proved that the accuracy of applying the deep fusion network that fused with deep features and handcrafted features is 30% higher than that of the ResNet50 alone, and that the time it consumes is reduced by 10 epoches through the gradient freezing method. Moreover, several experiments carried out on dataset Marketl501 indicate that under Single Query on Marketl501, Rankl(the probability of matching successfully for the first time) can reach a high number of 81.74% and mAP(mean average Precision) of 68.75%.

Paper Details

Date Published: 14 August 2019
PDF: 9 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111791Y (14 August 2019); doi: 10.1117/12.2541005
Show Author Affiliations
Zi Liang, Northeastern Univ. (China)
Jianning Chi, Northeastern Univ. (China)
Ruzhao Hua, Northeastern Univ. (China)
Yue Zhang, Northeastern Univ. (China)
Wenhao Wang, Northeastern Univ. (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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